The present invention relates to spectral methods in general and, more particularly, to spectral imaging methods for cell classification, biological research, medical diagnostics and therapy, which methods are referred to hereinbelow as spectral bio-imaging methods. The spectral bio-imaging methods of the invention can be used to provide automatic and/or semiautomatic spectrally resolved morphometric classification (e.g., detection, grading) of neoplasm. Specifically, the methods can be used to detect the spatial organization and to qualify and quantify cellular and tissue constituents and structures associated with, for example, tumorogenesis, using, for example, light transmission microscopy combined with high spatial and spectral resolutions. Furthermore, the methods of the present invention can be used to detect cellular spatial organization and to quantify cellular and tissue natural constituents, domains and structures using light transmission, reflection, scattering and fluorescence emission strategies, with high spatial and spectral resolutions, and may therefore be employed for classification of cancer cells using what is referred herein as spectrally resolved morphometry. In particular the methods of the present invention can be used for classification of cells to developmental stages, and to qualify and quantify metabolic processes within cells. The method can further be used to develop new and more fine tuned indexes for neoplasm classification (including grading), which will eventually replace the existing indexes.
A spectrometer is an apparatus designed to accept light, to separate (disperse) it into its component wavelengths, and measure the lights spectrum, that is the intensity of the light as a function of its wavelength. An imaging spectrometer is one which collects incident light from a scene and measures the spectra of each pixel (i.e., picture element) thereof.
Spectroscopy is a well known analytical tool which has been used for decades in science and industry to characterize materials and processes based on the spectral signatures of chemical constituents. The physical basis of spectroscopy is the interaction of light with matter. Traditionally, spectroscopy is the measurement of the light intensity emitted, transmitted, scattered or reflected from a sample, as a function of wavelength, at high spectral resolution, but without any spatial information.
Spectral imaging, on the other hand, is a combination of high resolution spectroscopy and high resolution imaging (i.e., spatial information). Most of the works so far described concern either obtaining high spatial resolution information from a biological sample yet providing only limited spectral information, for example, when high spatial resolution imaging is performed with one or several discrete band-pass filters [See, Andersson-Engels el al. (1990) Proceedings of SPIE--Bioimaging and Two-Dimensional Spectroscopy, 1205, pp. 179-189], or alternatively, obtaining high spectral resolution (e.g., a full spectrum), yet limited in spatial resolution to a small number of points of the sample or averaged over the whole sample [See for example, U.S. Pat. No. 4,930,516, to Alfano et al.].
Conceptually, a spectral bio-imaging system consists of (i) a measurement system, and (ii) an analysis software. The measurement system includes all of the optics, electronics and the manner in which the sample is illuminated (e.g., light source selection), the mode of measurement (e.g., fluorescence or transmission), as well as the calibration best suited for extracting the desired results from the measurement. The analysis software includes all of the software and mathematical algorithms necessary to analyze and display important results in a meaningful way.
Spectral imaging has been used for decades in the area of remote sensing to provide important insights in the study of Earth and other planets by identifying characteristic spectral absorption features. However, the high cost, size and configuration of remote sensing spectral imaging systems (e.g., Landsat, AVIRIS) has limited their use to air and satellite-born applications [See, Maymon and Neeck (1988) Proceedings of SPIE--Recent Advances in Sensors, Radiometry and Data Processing for Remote Sensing, 924, pp. 10-22; Dozier (1988) Proceedings of SPIE--Recent Advances in Sensors, Radiometry and Data Processing for Remote Sensing, 924, pp. 23-30 ]
There are three basic types of spectral dispersion methods that might be considered for a spectral bio-imaging system: (i) spectral grating or prism, (ii) spectral filters and (iii) interferometric spectroscopy. As will be described below, the latter is best suited to implement the method of the present invention, yet as will be appreciated by one ordinarily skilled in the art, grating, prism and filters based spectral bio-imaging systems may also be found useful in some applications.
In a grating or prism (i.e., monochromator) based systems, also known as slit-type imaging spectrometers, such as for example the DILOR system: [see, Valisa et al. (September 1995) presentation at the SPIE Conference European Medical Optics Week, BiOS Europe '95, Barcelona, Spain], only one axis of a CCD (charge coupled device) array detector (the spatial axis) provides real imagery data, while a second (spectral) axis is used for sampling the intensity of the light which is dispersed by the grating or prism as function of wavelength. The system also has a slit in a first focal plane, limiting the field of view at any given time to a line of pixels. Therefore, a full image can only be obtained after scanning the grating (or prism) or the incoming beam in a direction parallel to the spectral axis of the CCD in a method known in the literature as line scanning. The inability to visualize the two-dimensional image before the whole measurement is completed, makes it impossible to choose, prior to making the measurement, a desired region of interest from within the field of view and/or to optimize the system focus, exposure time, etc. Grating and prism based spectral imagers are in use for remote sensing applications, because an airplane (or satellite) flying over the surface of the Earth provides the system with a natural line scanning mechanism.
It should be further noted that slit-type imaging spectrometers have a major disadvantage since most of the pixels of one frame are not measured at any given time, even though the fore-optics of the instrument actually collects incident light from all of them simultaneously. The result is that either a relatively large measurement time is required to obtain the necessary information with a given signal-to-noise ratio, or the signal-to-noise ratio (sensitivity) is substantially reduced for a given measurement time. Furthermore, slit-type spectral imagers require line scanning to collect the necessary information for the whole scene, which may introduce inaccuracies to the results thus obtained.
Filter based spectral dispersion methods can be further categorized into discrete filters and tunable filters. In these types of imaging spectrometers the spectral image is built by filtering the radiation for all the pixels of the scene simultaneously at a different wavelength at a time by inserting in succession narrow band filters in the optical path, or by electronically scanning the bands using acousto-optic tunable filters (AOTF) or liquid-crystal tunable filter (LCTF), see below. Similarly to the slit type imaging spectrometers equipped with a grating or prism as described above, while using filter based spectral dispersion methods, most of the radiation is rejected at any given time. In fact, the measurement of the whole image at a specific wavelength is possible because all the photons outside the instantaneous wavelength being measured are rejected and do not reach the CCD.
Tunable filters, such as AOTFs and LCTFs have no moving parts and can be tuned to any particular wavelength in the spectral range of the device in which they are implemented. One advantage of using tunable filters as a dispersion method for spectral imaging is their random wavelength access; i.e., the ability to measure the intensity of an image at a number of wavelengths, in any desired sequence without the use of filter wheels. However, AOTFs and LCTFs have the disadvantages of (i) limited spectral range (typically, .lambda..sub.max =2.lambda..sub.min) while all other radiation that falls outside of this spectral range must be blocked, (ii) temperature sensitivity, (iii) poor transmission, (iv) polarization sensitivity, and (v) in the case of AOTFs an effect of shifting the image during wavelength scanning, demanding careful and complicated registration procedures thereafter.
All these types of filter and tunable filter based systems have not been used successfully and extensively over the years in spectral imaging for any application, because of their limitations in spectral resolution, low sensitivity, and lack of easy-to-use and sophisticated software algorithms for interpretation and display of the data.
A method and apparatus for spectral analysis of images which have advantages in the above respects was disclosed in U.S. patent application Ser. No. 08/392,019 to Cabib et al., filed Feb. 21st, 1995, now U.S. Pat. No. 5,539,517, issued Jul. 23, 1996, which is incorporated by reference as if fully set forth herein, with the objective to provide a method and apparatus for spectral analysis of images which better utilizes all the information available from the collected incident light of the image to substantially decrease the required frame time and/or to substantially increase the signal-to-noise ratio, as compared to the conventional slit- or filter type imaging spectrometer and does not involve line scanning. According to this invention, there is provided a method of analyzing an optical image of a scene to determine the spectral intensity of each pixel thereof by collecting incident light from the scene; passing the light through an interferometer which outputs modulated light corresponding to a predetermined set of linear combinations of the spectral intensity of the light emitted from each pixel; focusing the light outputted from the interferometer on a detector array, scanning the optical path difference (OPD) generated in the interferometer for all pixels independently and simultaneously and processing the outputs of the detector array (the interferograms of all pixels separately) to determine the spectral intensity of each pixel thereof. This method may be practiced by utilizing various types of interferometers wherein the OPD is varied to build the interferograms by moving the entire interferometer, an element within the interferometer, or the angle of incidence of the incoming radiation. In all of these cases, when the scanner completes one scan of the interferometer, the interferograms for all pixels of the scene are completed.
Apparatuses in accordance with the above features differ from the conventional slit- and filter type imaging spectrometers by utilizing an interferometer as described above, therefore not limiting the collected energy with an aperture or slit or limiting the incoming wavelength with narrow band interference or tunable filters, thereby substantially increasing the total throughput of the system. Thus, interferometer based apparatuses better utilize all the information available from the incident light of the scene to be analyzed, thereby substantially decreasing the measuring time and/or substantially increasing the signal-to-noise ratio (i.e., sensitivity). The sensitivity advantage that interferometric spectroscopy has over the filter and grating or prism methods is known in the art as the multiplex or Fellgett advantage [see, Chamberlain (1979) The principles of interferometric spectroscopy, John Wiley and Sons, pp. 16-18and p. 263].
Consider, for example, the "whisk broom" design described in John B. Wellman (1987) Imaging Spectrometers for Terrestrial and Planetary Remote Sensing, SPIE Proceedings, Vol. 750, p. 140. Let n be the number of detectors in the linear array, m.times.m the number of pixels in a frame and T the frame time. The total time spent on each pixel in one frame summed over all the detectors of the array is nT/m.sup.2. By using the same size array and the same frame rate in a method according to the invention described in U.S. Pat. No. 5,539,517, the total time spent summed over all the detectors on a particular pixel is the same, nT/m.sup.2. However, whereas in the conventional grating or prism method the energy seen by every detector at any time is of the order of 1/n of the total, because the wavelength resolution is 1/n of the range, in a method according to the invention described in U.S. patent application Ser. No. 08/392,019 the energy is of the order of unity, because the modulating function is an oscillating function (e.g., sinusoidal (Michelson) or similar periodic function such as low finesse Airy function with Fabry-Perot) whose average over a large OPD range is 50%. Based on the standard treatment of the Fellgett advantage (or multiplex advantage) described in interferometry textbooks [for example, see, Chamberlain (1979) The principles of interferometric spectroscopy, John Wiley and Sons, pp. 16-18 and p. 263], it is possible to show that devices according to this invention have measurement signal-to-noise ratios which are improved by a factor of n.sup.0.5 in the cases of noise limitations in which the noise level is independent of signal (system or background noise limited situations) and by the square root of the ratio of the signal at a particular wavelength to the average signal in the spectral range, at wavelengths of a narrow peak in the cases the limitation is due to signal photon noise. Thus, according to the invention described in U.S. Pat. No. 5,539,517, all the required OPDs are scanned simultaneously for all the pixels of the scene in order to obtain all the information required to reconstruct the spectrum, so that the spectral information is collected simultaneously with the imaging information. This invention can be used with many different optical configurations, such as a telescope for remote sensing, a microscope for laboratory analysis, fundus cameras for retinal imaging, fiber optics and endoscopes for industrial monitoring and medical imaging, diagnosis, therapy and others.
In a continuation application (U.S. patent application Ser. No. 08/571,047 to Cabib et al., filed Dec. 12, 1995, which is incorporated by reference as if fully set forth herein) the objective is to provide spectral imaging methods for biological research, medical diagnostics and therapy, which methods can be used to detect spatial organization (i.e., distribution) and to quantify cellular and tissue natural constituents, structures, organelles and administered components such as tagging probes (e.g., fluorescent probes) and drugs using light transmission, reflection, scattering and fluorescence emission strategies, with high spatial and spectral resolutions. In U.S. patent application Ser. No. 08/571,047, the use of the spectral imaging apparatus described in U.S. Pat. No. 5,539,517 for interphase fluorescent in situ hybridization of as much as six loci specific probes (each loci located on a different chromosome) was demonstrated, as well as additional biological and medical applications.
Spectral bio-imaging systems are potentially useful in all applications in which subtle spectral differences exist between chemical constituents whose spatial distribution and organization within an image are of interest. The measurement can be carried out using virtually any optical system attached to the system described in U.S. Pat. No. 5,539,517, for example, an upright or inverted microscope, a fluorescence microscope, a macro lens, an endoscope and a fundus camera. Furthermore, any standard experimental method can be used, including light transmission (bright field and dark field), auto-fluorescence and fluorescence of administered probes, etc.
Fluorescence measurements can be made with any standard filter cube (consisting of a barrier filter, excitation filter and a dichroic mirror), or any customized filter cube for special applications, provided the emission spectra fall within the spectral range of the system sensitivity. Spectral bio-imaging can also be used in conjunction with any standard spatial filtering method such as dark field and phase contrast, and even with polarized light microscopy. The effects on spectral information when using such methods must, of course, be understood to correctly interpret the measured spectral images.
In the evaluation of infiltrating breast carcinomas, ductal and lobular carcinomas may present similar histological appearances [Azzopardi J G, Chepick O F, Hartmann W H, Jafarey N A, Lombart-Bosch A, Ozello L (1982). The World Health Organization histological typing of breast tumors. 2nd ed. Am J Clin Pathol 78:806-816]. Some quantitative histopathological variables have been identified by morphological methods as an aid to the differentiation between ductal and lobular carcinomas [Ladekarl M and Sorensen F B (1993). Quantitive histopathological variables in in situ and invasive ductal carcinoma of the breast. AMPIS 101(12):895-903]. The attempts to grade and to differentiate, or in other words to classify the tumors have been based mainly on nuclear morphology and chromatin structure [Ladekarl M and Sorensen F B (1993). Quantitive histopathological variables in in situ and invasive ductal carcinoma of the breast. AMPIS 101(12):895-903; Cornelisse C J, de Konig H R, Moolenaar A J (1984). Image and flow cytometric analysis of DNA content in breast cancer; relation to estrogen receptor content and lymph node involvement. Anal Quant Cytol Histol 4:9-18; Stenkvist B, Westman-Naeser S, Holmquist J (1978). Computerized nuclear morphology as an objective method for characterizing human cancer cell populations. Cancer Res 38:4688-4977; Dawson A E, Austin R E, Weinberg D S (1991). Nuclear grading of breast carcinoma by image analysis. Classification by multivariate and neural network analysis. Am J Clin Pathol 95:S29-S37]. Morphometric classification of other tumor types, such as but not limited to leukemias, lymphomas, sarcomas and other carcinomas [see, for example, Clarke A M, Reid W A and Jack A S (1993) Combined proliferating cell nuclear antigen and morphometric analysis in the diagnosis of cancerous lymphoid infiltrates. J. Clin. Pathol. 46:129-134] are also vastly implemented both in research medical practice.
Nevertheless, as was recently published following an NIH workshop which evaluated the reliability of histopathological diagnosis by the best pathologists in the field of cancer diagnostics, there is a discordance among expert pathologists in the diagnosis of neoplasm. Based on this workshop, it was concluded that histopathological decisionmaking is 100% subjective, regardless of the origin of specimen and that this state of affairs in histopathological diagnosis is not confined to a specific tumor, but is applicable to differential diagnosis in every organ. These conclusions were published in an editorial by A Bernard Ackerman (1996) entitled "Discordance among expert pathologists in diagnosis of melanocytic neoplasm", in Human pathology 27:1115-1116.
Close to 80% of breast carcinomas are of the ductal type [Aaltomaa S, Lipponen P: Prognostic factors in breast cancer (reviews). Int J Oncol 1:153, 1992; Toikkanen S, Jensuu H (1990). Prognostic factors and long-term survival in breast cancer in a defined urban population. APMIS 98:1005-1014]. The differentiation between ductal and lobular carcinomas has proven to be useful for evaluation of patient prognosis and determination of treatment [Ellis I O, Galea M, Broughton N, Locker A, Blaney R W and Elston C W (1992). Pathological prognostic factors in breast cancer: II Histological type; relationship with survival in a large study with long term follow-up. Histopathology 20:479-489; Eskelinen M, Lipponen P, Papinaho S, Aaltomaa S, IKosma V M, Klemi P (1992). DNA flow cytometry, nuclear morphometry, mitotic indices and steroid receptors as independent prognostic factors in female breast cancer. Int J Cancer 51:555-561; and Toikkanen S, Jensuu H (1990). Prognostic factors and long-term survival in breast cancer in a defined urban population. APMIS 98:1005-1014]. The tumors have some differences in clinical behavior and in the pattern of metastasis; lobular carcinoma is more multifocal and bilateral than ductal carcinoma [Azzopardi J G, Chepick O F, Hartmann W H, Jafarey N A, Lombart-Bosch A, Ozello L (1982). The World Health Organization histological typing of breast tumors. 2nd ed. Am J Clin Pathol 78:806-816], and patient survival expectancy is usually better [DiConstanzo D, Rosen P P, Gareen I, Franklin S, Lesser M (1990). Prognosis in infiltrating lobular carcinoma: an analysis of "classical" and variant tumors. Am J Surg Pathol 14:12-23; du Toit R S, Locker A P, Ellis I O, Elston C W, Nicholson R I, Robertson J F R (1991). An evaluation of differences in prognosis, recurrence patterns and receptor status between invasive lobular and other invasive carcinomas of the breast. Eur J Surg Oncol 17:251-257]. The two tumor types are morphologically different, cells of infiltrating lobular carcinoma are usually smaller than those of ductal carcinoma, less pleomorphic and have fewer mitotic figures. Infiltrating ductal carcinoma cells have more prominent nucleoli [Azzopardi J G, Chepick O F, Hartmann W H, Jafarey N A, Lombart-Bosch A, Ozello L (1982). The World Health Organization histological typing of breast tumors. 2nd ed. Am J Clin Pathol 78:806-816].
Some histological types of intraductal carcinoma have been recognized: comedo, cribriform, micropapillary and solid. All are recognized and classified by specific criteria and subdivided primarily by architectural pattern, cellular pleomorphism, and nuclear hyperchromasia [Page D L, Anderson T G (1987). Diagnostic histopathology of the breast. Edinburgh, Scotland: Churchill Livingstone, 120-157; Lagios M D (1990). Duct carcinoma in situ pathology and treatment. Surg Clin North Am 70:853-871; and Lennington W J, Jensen R A, Dalton L W, Page D L: Ductal carcinoma in situ of the breast: Heterogeneity of individual lesions. Cancer 73:118-124, 1994]. The survival expectancy for lobular carcinomas is usually better than that of ductal carcinomas [DiConstanzo D, Rosen P P, Gareen I, Franklin S, Lesser M (1990). Prognosis in infiltrating lobular carcinoma: an analysis of "classical" and variant tumors. Am J Surg Pathol 14:1223; du Toit R S, Locker A P, Ellis I O, Elston C W, Nicholson R I, Robertson J F R (1991). An evaluation of differences in prognosis, recurrencee patterns and receptor status between invasive lobular and other invasive carcinomas of the breast. Eur J Surg Oncol 17:251-257]. Lobular carcinomas are more often bilateral and multifocal [Ladekarl M, Sorensen F B: Prognostic, quantitive histopathologic variables in lobular carcinoma of the breast. Cancer 72:2602, 1993] and the pattern of metastasis from the tumors was found to be different. Unfortunately, histological classification of breast carcinomas is subjected to low reproducibility and attempts to classify morphological subtypes of lobular carcinomas with different prognoses, therefore seem futile [Ladekarl M, Sorensen F B: Prognostic, quantitive histopathologic variables in lobular carcinoma of the breast. Cancer 72:2602, 1993]. Both lobular and ductal types are now thought to arise from the terminal duct-lobular unit.
Characterization of nuclear features by different techniques is used for determination of diagnosis, treatment and prognosis. Quantitative estimation of various histopathological parameters such as two dimensional estimates of nuclear profile area, nuclear profile densities and mitotic profile numbers have been shown to correlate with differentiation and prognosis. Alterations in nuclear structure are the morphologic hallmark of cancer diagnosis. Nuclear size, shape, chromatin pattern have all been reported to change in breast cancer [Pienta K J, Coffey D S: Correlation of nuclear morphometry with progression of breast cancer. Nuclear Morphometry of breast cancer 2012, 1991]. However, heterogeneity in morphology and biology of tumors belonging to the same classification group has been found to be the most prominent feature of breast cancer [Komitowski D D and Janson C P (1990). Quantitive features of chromatin structure in the prognosis of breast cancer. Cancer 65:2725-2730].
Among 11 cytological parameters that were examined by de-las-Morenas et al. [de-las-Morenas A, Crespo P, Moroz K and Donnely M M (1995). Cytologic diagnosis of ductal versus lobular carcinoma of the breast. Acta Cytol 39(5):865-869] using an automated morphometric system on cytologic specimens, chromatic pattern, nuclear size and overall cell size were found to be statistically different between infiltrating lobular and infiltrating ductal carcinoma cell nuclei. Thus, the presence of coarsely granular chromatin, nuclear size of more than 44 .mu.m.sup.2 and cell size of more than 82 .mu.m.sup.2, were found to be related to ductal carcinoma.
Ladekarl and Sorensen found that the main three-dimensional nuclear size, the main nuclear profile area and the mitotic index were all significantly larger in ductal than in lobular carcinomas, whereas the main nuclear density index was smaller in ductal carcinoma [Ladekarl M, Sorensen F B: Prognostic, quantitive histopathologic variables in lobular carcinoma of the breast. Cancer 72:2602, 1993]. Yu et al. also identified some distinct nuclear features useful in the differentiation of infiltrating ductal and lobular carcinoma [Yu G H, Sneige N, Kidd L D, Johnston and Katz R L (1995). Image analysis derived morphometric differences in fine needle aspirated of ductal and lobular breast carcinoma. Anal Quant Cytol Histol 17(2):88-92].
All these methods, however, employ only image (i.e., spatial) information for analysis. A whole new dimension of analysis may be added using spectral information which reflects the interaction between light and matter. The combination of both spatial and spectral information will largely contribute to cancer detection and classification.
There is thus a widely recognized need for, and it would be highly advantageous to have, spectral bio-imaging methods and spectral morphometric methods for cells classification devoid of the above described limitations, especially the subjectiveness of pathologists in neoplasm diagnosis, which provide advanced and quantitative, semi or fully automatic, means for cancer classification.